Control Rights and Capital Structure: An Empirical ...faculty.chicagobooth.edu/amir.sufi/research/papers/Roberts_Sufi_JF... · THE JOURNAL OF FINANCE •VOL. LXIV, NO. 4 AUGUST 2009

THE JOURNAL OF FINANCE • VOL. LXIV, NO. 4 • AUGUST 2009

Control Rights and Capital Structure:An Empirical Investigation

MICHAEL R. ROBERTS and AMIR SUFI∗

ABSTRACT

We show that incentive conflicts between firms and their creditors have a large impacton corporate debt policy. Net debt issuing activity experiences a sharp and persistentdecline following debt covenant violations, when creditors use their acceleration andtermination rights to increase interest rates and reduce the availability of credit. Theeffect of creditor actions on debt policy is strongest when the borrower’s alternativesources of finance are costly. In addition, despite the less favorable terms offered byexisting creditors, borrowers rarely switch lenders following a violation.

A FUNDAMENTAL QUESTION IN FINANCIAL ECONOMICS is: How do firms choose their fi-nancial policies? Extant empirical research on this question has focused primar-ily on the presence of taxes and bankruptcy costs (e.g., Scott (1976)), informationasymmetry (e.g., Myers and Majluf (1984)), and more recently, market timingbehavior (e.g., Baker and Wurgler (2002)). However, beginning with Jensen andMeckling (1976), a large body of theoretical research examines how incentiveconflicts between managers and external investors affect corporate financialpolicies. In particular, theoretical research on financial contracting shows thatin the presence of incentive conflicts, optimal debt contracts will allocate cer-tain rights to creditors after negative performance in order to help firms securefinancing ex ante (e.g., Aghion and Bolton (1992) and Dewatripont and Tirole

∗Michael R. Roberts is from the Wharton School, University of Pennsylvania. Amir Sufi is fromthe Booth School of Business, University of Chicago and NBER. We are especially grateful to theeditors, Campbell Harvey and John Graham, and two anonymous referees for helpful comments.We also thank Bo Becker (discussant); Alex Butler; Harry DeAngelo; Linda DeAngelo; Peter De-Marzo; Doug Diamond; Christopher Hennessy; Victoria Ivashina (discussant); Steve Kaplan; AnilKashyap; Mark Leary; Hayne Leland; Mike Lemmon; Atif Mian; Mitchell Petersen (discussant);Raghu Rajan; Josh Rauh; Morten Sorensen; Roberto Wessels; Julie Xu; and Jeffrey Zwiebel; semi-nar participants at Baruch College, the Federal Reserve Bank of Philadelphia, Rutgers University,the Texas Finance Festival, Texas A&M, the University of California at Berkeley, the University ofChicago, the University of Colorado, the University of Michigan, the University of Pennsylvania,the University of Southern California, and the University of Vienna; and conference participantsat the 2007 NYU Stern/NY Fed Conference on Financial Intermediation, 2007 Texas Finance Fes-tival, 2007 Stanford Institute for Theoretical Economics, 2007 Washington University CorporateFinance Conference, and 2007 NYU/NY Fed Conference on Financial Intermediation for helpfuldiscussions. We also thank Rahul Bhargava, Ali Khan, Wang Yexin, and Lin Zhu for excellentresearch assistance. Roberts gratefully acknowledges financial support from a Rodney L. WhiteGrant and an NYSE Research Fellowship.

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(1994)). Thus, in the context of the capital structure debate, financial contract-ing theory raises two important empirical questions: First, to what extent doincentive conflicts and creditor rights impact corporate financial policies? And,second, how do incentive conflicts and creditor rights impact corporate financialpolicies?

The goal of this study is to answer these questions by examining the responseof corporate financial policies to covenant violations. Covenant violations pro-vide a unique opportunity for studying incentive conflicts, creditor rights, andfinancial policy for several reasons. First, the presence of covenants in debtagreements is motivated by their ability to mitigate incentive conflicts be-tween managers and creditors (Smith and Warner (1979)). Second, covenantviolations give creditors the right to demand immediate repayment and with-hold further credit, thereby providing a potential channel through which themisalignment of incentives can impact financial policy (Tirole (2006)). Third,covenant violations occur frequently (Dichev and Skinner (2002)) and rarelylead to payment default or bankruptcy (Gopalakrishnan and Parkash (1995)),suggesting that violations are a potentially important concern for firms evenoutside of financial distress. Finally, the discrete nature of a covenant violationenables us to employ a regression discontinuity design that helps identify theeffect of violations on financial policies.

Our analysis centers on a novel data set that includes the universe of fi-nancial covenant violations reported on firms’ annual and quarterly securitiesand exchange commission (SEC) filings between 1996 and 2005. Among thepopulation of publicly listed firms in the United States, we find that morethan one-quarter violate a financial covenant at some point during our sam-ple horizon. This high incidence of covenant violations in the population ofpublicly listed firms complements previous evidence that documents a simi-lar frequency among firms that utilize private credit agreements (Dichev andSkinner (2002)). More importantly, the high incidence in the general popula-tion implies that covenant violations are relevant for a large number of publicfirms.

Our first set of results shows that, on average, financial policy exhibits asharp change following a covenant violation. Specifically, net debt issuing ac-tivity experiences a large and persistent drop immediately after the violation.In the four quarters before a violation, borrowers have an average net debtissuance scaled by lagged assets of 80 basis points per quarter. In just twoquarters after the violation, net debt issuance falls to −25 basis points. Inother words, firms move from increasing net debt issuance by 0.8% of as-sets per quarter to reducing net debt issuance in just two quarters. Further,this decline is persistent, lasting for over 2 years after the violation, andleads to a corresponding decline in leverage of over 3%. While the change tothe stock of debt in firms’ capital structures is modest relative to the typicalunconditional variation in leverage ratios, the change to the flow of debt islarge. In fact, the drop in net debt issuance after a covenant violation moves afirm from the 75th percentile of the net debt issuance distribution to the 35th

percentile.

Control Rights and Capital Structure 1659

To help isolate the impact of the covenant violation, we turn to a firm and pe-riod fixed effects regression framework. We show that net debt issuing activitydeclines by approximately 70 basis points in the quarter immediately after acovenant violation—a marginal effect larger than that of most previously iden-tified capital structure determinants. For example, a two standard deviationchange in the size of the firm, the single most powerful predictor of net debtissuing activity, results in only a 52 basis point quarterly decline in net debtissuances. Further, the 70 basis point drop in net debt issuance moves the firmfrom the 65th to 35th percentile of the within-firm net debt issuance distribu-tion. The persistence of this decline, even after conditioning on fixed effectsand traditional control variables, translates into a decline in leverage ratiosover the 2 years following the violation that moves firms from the 70th to the45th percentile of the within-firm leverage distribution. Thus, while covenantviolations are responsible for only a modest fraction of cross-sectional variationin leverage ratios, violations lead to significant time-series variation in lever-age ratios despite shrinking asset bases that offset the reduction in net debtissuances (Chava and Roberts (2008) and Nini, Smith, and Sufi (2009)).

Both the magnitude and statistical significance of the financing response tocovenant violations are robust to a variety of additional tests aimed at ensur-ing that the estimated response is free from confounding influences, such aschanges in investment opportunities or expected bankruptcy costs that mayoccur around the time of the violation. For example, we incorporate paramet-ric and nonparametric functions of the variables on which financial covenantsare often written to account for the possibility that these measures containinformation about managers’ preferences for issuing debt. We also show thatleverage rebalancing (Leary and Roberts (2005)) and mean reversion in lever-age ratios (Flannery and Rangan (2006), Kayhan and Titman (2007)) are notbehind our findings, as violators with relatively low leverage ratios reduce netdebt issuance by more than nonviolators with high leverage ratios. Finally, inorder to control for the possible endogeneity of the covenant threshold and fur-ther control for differences between violators and nonviolators, we undertakea regression discontinuity design that reveals a nearly identical decline in netdebt issuing activity following a covenant violation.

After establishing the average effect of covenant violations on financial pol-icy, our second set of results attempts to understand how violations impactfinancial policy by identifying the underlying mechanism linking the violationto the subsequent policy response. To do so, we first examine heterogeneity inthe effect of violations on financial policy. For example, we find that net debtissuances decline by almost 150 basis points following a new covenant viola-tion, which we define as violations for firms that have not violated a covenantin the previous four quarters, whereas subsequent violations are followed by a30 basis point decline. We also find that the decline in net debt issuances is sig-nificantly larger for firms with high leverage and low market-to-book ratios atthe time of the violation. Similarly, the decline in net debt issuances is approx-imately 115 basis points larger for firms lacking a credit rating. These resultssuggest that (1) the granting of acceleration and termination rights to existing


creditors has an immediate impact on the provision of credit, and (2) the impactof this rights transfer on financial policy is greatest when alternative sourcesof capital are relatively expensive or limited.

We next examine the SEC filings of a random subsample of violators in orderto identify how creditors use the control rights obtained after the violation toinfluence lending terms. We find that one out of three violators explicitly statesthat creditors respond to the violation by reducing the credit facility, increasingthe interest spread, or demanding additional collateral. Among violators thatreport creditor action, we find that net debt issuance scaled by lagged assetsdeclines by 418 basis points in just two quarters. Additionally, our examinationof SEC filings reveals that only 4% of violators terminate their relationshipwith existing creditors within two quarters after the violation. In other words,despite the unfavorable terms offered by existing lenders, very few borrowersrepay the violated agreement with financing from other lenders. Thus, financialcovenant violations have a large effect on financing decisions because violatorsare unable to obtain financing from other lenders at more favorable terms,thereby subjecting borrowers to the disciplinary actions of their existing credi-tors.

Our primary contribution to the capital structure literature is to show thatincentive conflicts in conjunction with the transfer of control rights have a first-order effect on financial policy. In this sense, our study is related to those byJung, Kim, and Stulz (1996), Berger, Ofek, and Yermack (1997), and Garveyand Hanka (1999), who show that measures of managerial entrenchment ordiscretion are correlated with security issuance decisions. In contrast to thesestudies, we focus on the conflict of interest between managers and creditors.Additionally, we quantify the relative magnitude of the effect of incongruentincentives on financial policy, and we show a precise mechanism (covenant vi-olations and the resulting transfer of control rights) through which incentiveconflicts impact financial policy. As such, our study confirms several hypothe-ses from the financial contracting literature (Aghion and Bolton (1992) andDewatripont and Tirole (1994)), which has received little attention from empir-ical capital structure studies.

Our findings are also related to a growing body of research showing that thesupply of capital has an important effect on firm financial policy (Faulkenderand Petersen (2006), Leary (2006), Sufi (2009a), Lemmon and Roberts (2007)).Unique to our study is the finding that debt covenant violations enable a firm’sexisting creditors to address incentive conflicts by moderating the supply ofcapital. More precisely, we show that borrowers rarely switch lenders after aviolation, and as a result, changes in the willingness to supply credit by existinglenders lead to significant effects on financial policy.

Finally, our findings are related to empirical research examining covenant vi-olations and the resolution of technical default (e.g., Smith and Warner (1979),Beneish and Press (1993, 1995), Chen and Wei (1993), Sweeney (1994), Chavaand Roberts (2008), Nini, Smith, and Sufi (2009), Sufi (2009b)). While thesestudies show that existing creditors use their acceleration right to extractamendment fees, reduce unused credit availability, increase interest rates, andinfluence investment, we are the first to explore how covenant violations fit


into the broader capital structure debate.1 In particular, we are the first toshow that covenant violations—via the allocation of control rights—are associ-ated with a large and persistent decline in the flow of debt and a correspondingdecline in leverage ratios. Additionally, we are the first to focus attention on theidentification of this effect using a novel empirical technique aimed at show-ing that the estimated financial response is a consequence of creditor actionsfollowing the covenant violation (see Smith (1993)).

The remainder of the paper proceeds as follows. Section I describes our data,presenting summary statistics in the process. Section II lays the theoreticalfoundation and motivation for our study. Sections III and IV present the results.Section V concludes.

I. Data

A. Sample Construction

We begin with all nonfinancial Compustat firm-quarter observations from1996 to 2005. We choose 1996 as the start year for our sample construction tocoincide with the imposition of the SEC’s requirement that all firms submittheir filings electronically, a feature that we require to measure covenant viola-tions among the population of publicly traded firms. To ensure the continuity ofour sample across all of our study, we condition on the presence of both period tand t−1 data for all of the variables considered in our analysis.2 (All variablesused in this study are formally defined in the Appendix.) To mitigate the impactof data errors and outliers on our analysis, we Winsorize all variables at the 5th

and 95th percentiles, though our results are largely unaffected if we Winsorizeat the 1st and 99th percentiles. Finally, because our primary analysis relies onwithin-firm variation, we include only firms for which there are at least fourconsecutive quarters of available data. In concert, these criteria reduce thesample from 176,993 firm-quarter observations to 135,736 firm-quarter obser-vations.

We supplement the Compustat data with information on financial covenantviolations collected directly from 10-K and 10-Q SEC filings. These data areavailable given SEC Regulation S-X, which requires that “any breach of acovenant of a[n] . . . indenture or agreement which . . . exist[s] at the date of themost recent balance sheet being filed and which has not been subsequentlycured, shall be stated in the notes to the financial statements” (SEC (1988), asquoted by Beneish and Press (1993, p. 236)). As Sufi (2009b) notes, the SEChas reinforced this requirement in recent interpretations: “companies that are,

1 The fact that covenant violations lead to reduced credit and increased interest rates fromexisting lenders does not necessarily mean that covenant violations affect capital structure. Forexample, firms could switch to a new lender after the violation, or firms could reduce net debt andnet equity issuances in equal proportion. Our analysis quantifies the magnitude and persistenceof the costs associated with covenant violations via their impact on financial policy.

2 More precisely, we require for each firm-quarter observation nonmissing data for both thecontemporaneous and lagged value of total assets, total sales, tangible assets, total debt, net worth,cash holdings, net working capital, EBITDA, cash flow, net income, interest expense, market-to-book ratio, book value of equity, and market value of equity.


or are reasonably likely to be, in breach of such covenants must disclose mate-rial information about that breach and analyze the impact on the company ifmaterial (SEC (2003)).”

In order to extract these data, we first match all Compustat quarterly obser-vations to their respective 10-Q or 10-K filing based on the IRS identificationnumber. We then use a Perl program to search the filings for one of 20 terms(see the Appendix). Each time the program finds a term, it prints the 10 linesbefore and after the term in a separate document. We check each passage to en-sure that the existence of the term reflects a financial covenant violation. Thus,each firm-quarter observation in our sample either is or is not in violation of acovenant.

As Dichev and Skinner (2002) note, financial covenant violations that arereported by firms in their SEC filings likely represent situations in which theywere unable to obtain an amendment or waiver to cure the violation by theend of the reporting period. While this is in general correct, it is important tonote that many of the violations reported in SEC filings are violations that arewaived before the reporting period ends. In these cases, the firm voluntarilyreports that it was in violation during the reporting period even though it hascured the violation by the end of the reporting period. One potential concernis that the reported violations tracked in our data represent, on average, moreserious violations than violations that could be cured before the end of the re-porting period. However, a comparison of observable measures of credit qualityand investment around the initial reported covenant violation in our sampleversus the initial violation in previous studies reveals very similar patterns.For example, cash flow and capital expenditures show patterns around thefirst reported violation in our sample that are almost identical to those foundin studies by Dichev and Skinner (2002) and Chava and Roberts (2008), whichsuggests that initial reported violations in our sample correspond closely toinitial actual violations. Further, in our robustness tests, we explicitly addressthis concern with a subsample of firms for which we can observe both reportedand unreported violations.

B. Summary Statistics

Although the SEC requires firms to report unresolved financial covenant vi-olations, it does not require firms to detail exactly which covenant has beenviolated. To give a sense of the types of financial covenants employed in privatecredit agreements, we examine the financial covenants contained in a sub-sample of 3,603 private credit agreements entered into by 1,894 of the firmsin our sample.3 Almost 97% of these credit agreements contain at least onefinancial covenant, which can be broadly categorized by the accounting mea-sures on which they are based: debt to cash flow (58%), debt to balance sheet

3 A table corresponding to this information is available in an Internet Appendix available at:http://www.afajof.org/supplements.asp. For more details on these private credit agreements andhow they were obtained, see Nini, Smith, and Sufi (2009). There are slightly fewer observations inTable I than in Nini, Smith, and Sufi (2009) given that some agreements detail financial covenantsin an attached exhibit that is not included in the SEC filing.


items (29%), coverage ratios (74%), net worth (45%), liquidity (15%), and cashflow (13%). Among these agreements, 79% contain a financial covenant thatrestricts a ratio with debt in the numerator, and 74% contain a covenant thatrestricts a coverage ratio limiting the amount of interest payments. Overall,almost 90% of the credit agreements contain either an explicit or implicit re-striction on the borrower’s total debt, highlighting the importance of financialcovenants in the borrower’s capital structure determination.

Panel A of Table I reveals that covenant violations are common. Over one-quarter of the firms in our sample experience a financial covenant violation atsome point between 1996 and 2005. Among firms with an average leverage ratioof at least 5%, the percentage of covenant violators increases to 30%. Panel Aalso shows that firms across all industries violate financial covenants with sim-ilar proportions, with the possible exception of firms in Trade-Wholesale. Firmswith and without a corporate credit rating violate covenants at approximatelysimilar rates as well. However, smaller firms are significantly more likely toviolate financial covenants than larger firms; firms with total assets less than$100 million are almost 20 percentage points more likely to violate a financialcovenant than firms with total assets over $5 billion.

Panel B presents the 1-year probabilities of violating a financial covenant inour sample based on the S&P corporate credit rating. Firms rated “A” or betterhave a 1-year probability of violating a covenant of 1%, while firms rated BBhave a 7% probability. Relative to the 1-year payment default probabilities re-ported by S&P, the probabilities of a covenant violation are significantly largerin every rating category except firms rated “CCC” or worse, which includessome firms that have already defaulted on a payment. The difference in theprobabilities is particularly large for firms rated “BB” or better. Thus, evenfirms that are unlikely to default on payments face a nontrivial probability ofviolating a financial covenant.

Table II presents the summary statistics for our outcome variables (net secu-rity issuance and book leverage), our “covenant control variables,” and “othercontrol variables.” For presentation purposes, we focus our attention on netdebt issuance computed from the change in balance sheet debt and net equityissuance computed from the statement of cash flows. However, we also examinenet debt issuance computed from the statement of cash flows and net equityissuance computed from the split-adjusted change in shares outstanding (Famaand French (2005)). The results are qualitatively similar and therefore not re-ported.

The covenant control variables include many of the accounting ratios onwhich financial covenants are written. As such, they provide a means to controlfor variation in accounting variables that are correlated with both the violationevent and the propensity to issue debt. The third group, other control variables,contains several additional control variables suggested by the empirical capitalstructure literature (e.g., Frank and Goyal (2005)) as being relevant for finan-cial policy. Overall, the means and medians, after annualizing flow variables,coincide with those found in previous studies investigating capital structure(e.g., Frank and Goyal (2003) and Mackay and Phillips (2005)).


Table ICovenant Violations

Panel A of this table presents the percentage of firms that report a financial covenant violationin 10-K or 10-Q SEC filings at some point between 1996 and 2005. Panel B reports the 1-yearprobability of a financial covenant violation and a payment default according to S&P. S&P 1-yearcumulative default probabilities are equal-weighted averages over ratings to get the probability forthe broad rating class. The sample includes 6,381 firms and 135,736 firm-quarter observations.

Panel A: Fraction of Firms That Violate Financial Covenant

Percentage of FirmsReporting Violation

TotalsTotal sample 25.6%Firms with average book leverage ratio greater than 0.05 30.0%

By industryAgriculture, minerals, construction 28.6%Manufacturing 25.4%Transportation, communication, and utilities 25.2%Trade—wholesale 34.8%Trade—retail 23.3%Services 24.6%

By size (book assets)Less than $100M 28.8%$100M to $250M 28.8%$250M to $500M 25.0%$500M to $1,000M 21.7%$1,000M to $2,500M 18.7%$2,500M to $5,000M 17.8%Greater than $5,000M 10.6%

Borrower does not have credit rating 26.6%Borrower has credit rating 22.3%

Panel B: 1-Year Probabilities of Default by Credit Rating

1-Year Probability of S&P 1-Year CumulativeCovenant Violation Default Probability

A or better 1.0% 0.0%BBB 3.1% 0.2%BB 6.8% 0.9%B 9.4% 7.2%CCC or worse 18.4% 21.9%Unrated 10.0%

II. The Consequences of Covenant Violations: Practice and Theory

A. Financial Covenants and Creditor’s Rights

Before discussing the theoretical motivation for why covenant violationsmight impact firms’ financial policies, we first clarify what happens when afinancial covenant is violated. Provisions in the contract grant creditors the


Table IISummary Statistics

This table presents summary statistics for the unbalanced panel of 6,381 firms from 1996 to 2005(135,736 firm-quarters). Net debt issuance and net equity issuance are scaled by lagged assets.

Mean Median SD

Capital structure variablesNet debt issuance (basis points) 50.5 0.0 400.8Net equity issuance (basis points) 39.8 0.4 166.8Book debtt/assetst 0.228 0.182 0.221

Covenant control variablesNet wortht/assetst 0.495 0.518 0.287Net working capitalt/assetst 0.254 0.235 0.271Casht/assetst 0.199 0.092 0.231EBITDAt/assetst−1 0.006 0.026 0.068Cash flowt/assetst−1 −0.007 0.017 0.074Net incomet/assetst−1 −0.022 0.006 0.077Interest expenset/assetst−1 0.005 0.003 0.006

Other control variablesMarket-to-book ratiot 2.338 1.572 1.947Tangible assetst/assetst 0.270 0.194 0.230Ln(assetst) 4.900 4.910 2.384

right to immediately accelerate outstanding amounts in response to a viola-tion, also known as a technical default. In addition, a violation gives creditorsthe right to terminate any unused portion of lines of credit or revolving creditfacilities. As an illustrative example, the loan contract between Digitas Inc.and Fleet National Bank, originated on July 25, 2000, contains the followingclauses, which are standard in most private credit agreements.4

14.1. Events of Default and Acceleration. If any of the followingevents . . . shall occur: (c) the Borrower shall fail to comply with any ofits covenants contained in [the section describing financial covenants]; . . .Then . . . [Fleet] may . . . by notice in writing to the Borrower declare allamounts owing with respect to this Credit Agreement, the RevolvingCredit Notes and the other Loan Documents and all Reimbursement Obli-gations to be, and they shall thereupon forthwith become, immediately dueand payable without presentment, demand, protest or other notice of anykind . . .

14.2. Termination of Commitments. If any one or more of the Events ofDefault . . . shall occur, any unused portion of the credit hereunder shallforthwith terminate and each of the Banks shall be relieved of all furtherobligations to make Revolving Credit Loans to the Borrower and the Agentshall be relieved of all further obligations to issue, extend or renew Lettersof Credit.

4 Excerpt is from the 10-Q SEC filing of Digitas Inc. for September 30, 2000 available at http://www.sec.gov/Archives/edgar/data/1100885/000091205700049556/0000912057-00-049556.txt.


While private credit agreements give creditors the right to accelerate out-standing balances in response to technical defaults, extant research suggeststhat most technical defaults lead to renegotiation and waivers of the violation,as opposed to acceleration of the loan (e.g., Gopalakrishnan and Parkash (1995),Chen and Wei (1993), Beneish and Press (1993)). However, extant research alsofinds that creditors use their acceleration right to extract amendment fees,reduce unused credit availability, increase interest rates, increase reportingrequirements, increase collateral requirements, and restrict corporate invest-ment (Gopalakrishnan and Parkash (1995), Chen and Wei (1993), Sufi (2009b),Chava and Roberts (2008), and Nini, Smith, and Sufi (2009)). Thus, accompa-nying covenant violations are a wide range of actions undertaken by creditors,which are largely removed from acceleration of the loan or bankruptcy.

B. Theory and Hypothesis Development

Why would covenant violations affect capital structure? To answer this ques-tion and motivate our empirical analysis, we focus on theoretical research inwhich covenants play a crucial role in mitigating incentive conflicts betweenmanagers and external investors. The foundation of this literature is Jensenand Meckling (1976), who analyze how risk-shifting tendencies of managersacting on behalf of shareholders influence debt contracts. Given incentive con-flicts introduced by managers’ convex payoff functions, creditors will attemptto mitigate risk-shifting through covenants restricting firm investment andfinancial policy.

Covenants emerge endogenously in recent theoretical research that derivesdebt with control rights as an optimal financial contract. For example, Aghionand Bolton (1992) use an incomplete contracting framework in which a wealth-constrained owner-manager seeks capital to finance projects that produce bothcash profits and managerial private benefits. In their model, origination con-tracts allocate a decision right that depends on an imperfect state signal. Whenthe signal indicates that managerial private benefits are likely to distort themanager into inefficient decisions, the decision right is transferred to creditors(see also Zender (1991)).

Dewatripont and Tirole (1994) assume the existence of an ex ante manage-rial moral hazard problem, and they find that optimal financial contracts withconcave cash flow rights encourage debt-holders to interfere with firm policyafter signs of poor performance. Creditor interference serves as a managerialdisciplining device, and therefore helps mitigate moral hazard problems. Intheir model, a noisy signal correlated with firm performance is contractible,and creditors interfere with firm policy conditional on negative realizations ofthe signal.

Although the allocation of control rights is an important aspect of thesemodels, creditor “control” does not necessarily entail creditors literally re-placing managers as decision-makers. To the contrary, control rights in theDewatripont and Tirole (1994) and Aghion and Bolton (1992) frameworks referto limited rights given to creditors after negative performance. For example, in


Dewatripont and Tirole (1994), creditors may obtain the right to force reorgani-zation, divest, choose a conservative option, or stop a specific project. Covenantviolations are a close empirical analog to these models on two dimensions. First,creditors receive termination and acceleration rights following negative perfor-mance (i.e., violating a covenant). Second, the acceleration and terminationrights that creditors obtain after a violation are limited rights that allow credi-tors to protect the value of their claim. However, the rights do not give creditorsthe ability to make financing decisions or run the firm.5

Likewise, a covenant violation by itself does not generate changes in finan-cial policy. Instead, a violation is the impetus that leads to changes in financialpolicy because of the accompanying transfer of control rights and creditor inter-vention. For example, the covenant violation gives creditors the opportunity toexamine the firm more carefully, and, even more importantly, the control rightsassociated with the violation give creditors the ability to influence financial pol-icy if changes in circumstances warrant such intervention. Consequently, ouranalysis is comprised of two sections: The first identifies the response of fi-nancial policy to covenant violations, and the second identifies the underlyingeconomic mechanism behind this relation.

III. The Effect of Covenant Violations on Financing Decisions

A. Graphical Analysis

We begin our analysis of whether covenant violations affect financing deci-sions by illustrating the relationship between violations and net debt issuance,net equity issuance, and leverage ratios. Panels A to C of Figure 1 present theunconditional averages for these outcomes in event time, where time zero isthe quarter of the violation. In order to clearly identify the effect of a violationon financial policy, we require that the firm not experience another violationin the 12 quarter window surrounding the event. That is, from four quartersprior to the violation until eight quarters after the violation, the firm expe-riences only one violation indicated in the figures by period zero. While theanalysis present in the figures is isolated to a subsample, the regression anal-ysis beginning in the next section examines all violations, and the analysis inSection IV explicitly differentiates between initial and subsequent violations.Additionally, to ensure that our results are not an artifact of a changing samplecomposition, we focus on the subsample of firms surviving for at least 2 yearsafter the violation. However, the results are qualitatively similar if we removethis requirement.

Panel A examines the effect of covenant violations on the flow of debt capitalas measured by net debt issuance scaled by lagged assets. Before the viola-tion, there is no discernable trend in net debt issuance. Immediately followingthe violation, firms experience a sharp decrease in net debt issuance. By the

5 Kaplan and Stromberg (2003) find that venture capital contracts often allocate board seatsand voting rights to VCs following negative performance, which they interpret as support for theAghion and Bolton (1992) framework. However, in our setting, creditors do not receive board seatsor voting rights after covenant violations.


Panel A: The Effect of a Covenant Violation on Average Net Debt Issuance

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Panel B: The Effect of a Covenant Violation on Average Net Equity Issuance

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Panel C: The Effect of a Covenant Violation on Average Leverage Ratio

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Figure 1. The effect of a covenant violation on capital structure. Panels A, B, and C ofFigure 1 show the effect of a covenant violation on net debt issuance, net equity issuance, and theleverage ratio, respectively. The dotted lines define the 90% confidence interval for the mean. Thesample includes borrowers that violate a covenant only once (at time t = 0) during the 12-quarterwindow.


second-quarter after the covenant violation, net debt issuance activity falls from+95 basis points to −23 basis points, for an effect of almost 120 basis pointsin just two quarters. This decline is statistically significant at all conventionallevels; it is also economically large, corresponding to an annualized decline inthe net flow of debt equal to almost 2.5% of lagged assets. This sharp drop innet debt issuance after the violation moves the average violator from the 75th

to the 35th percentile of the unconditional distribution. This change in net debtissuance policy also shows persistence. Even 2 years later, net debt issuanceis significantly lower than it was in the five quarters up to and including thequarter of the covenant violation.

While these magnitudes are large, it is important to emphasize that theanalysis in Figure 1 likely underestimates the effect of the violation on financialpolicy given that we cannot measure unused capacity under bank revolvingcredit facilities. Sufi (2009b) finds a reduction in unused lines of credit scaled byassets of 360 basis points in the year after a covenant violation, which implies a90 basis point reduction per quarter. Firms generally do not detail their unusedrevolving credit capacity at the quarterly frequency, which is why we focus onthe actual flow of debt.

Panel B presents the results for net equity issuances. Unlike net debt is-suances, there is no discernible change in net equity issuances right after thecovenant violation. There is some evidence of an increasing trend following theviolation; however, it is statistically weak and economically small.

Panel C examines the effect of the violation on book leverage ratios.6 Bythe fifth-quarter after the violation, firm leverage is statistically significantlylower than in the quarter of the violation. In fact, by the eighth-quarter after thecovenant violation, firm leverage is statistically significantly lower than that inthe quarter before the covenant violation. From the first to the eighth-quarterafter the violation, violators reduce their leverage by 5%—a significant declinerelative to the typical within-firm variation in leverage ratios but a modest de-cline when compared to the typical cross-sectional variation in leverage ratios.7

Ultimately, these findings suggest that covenant violations are not respon-sible for much of the total variation in leverage ratios, the majority of whichis comprised of between-firm variation corresponding to differences in averageleverage ratios across firms (Lemmon, Roberts, and Zender (2008)). Looselyspeaking, a relatively high levered firm is unlikely to become a relatively lowlevered firm because of a covenant violation, in part because of a contractingasset base. However, violations do generate large changes in net debt issuances(i.e., the flow of debt) and, potentially, large changes in leverage ratios relative

6 The effect of covenant violations on market leverage ratios (unreported) is almost identical.7 The violation moves a firm from the 72nd to 45th percentile of the within-firm distribution of

leverage ratios, but only from the 66th to the 58th percentile of the overall leverage distribution—lessthan one decile. One reason for the muted effect on leverage ratios is that covenant violations leadto significant reductions in assets. Specifically, negative cash flow shocks (Sufi (2009b)), asset sales(Beneish and Press (1993)), and reduced capital expenditures (Chava and Roberts (2008)) followingcovenant violations work to reduce assets and increase leverage, thereby partially offsetting theeffects of reduced net debt issuances.


to the typical within-firm variation. The analysis below investigates these ef-fects more closely.

B. Identification and Empirical Strategy

While the results in Panels A and C of Figure 1 are consistent with a signif-icant effect of violations on financial policy, the extent to which the observedchange is due to the covenant violation, as opposed to changes in managers’preferences for debt, is unclear. In particular, our primary identification con-cern is that changes in the firm surrounding a covenant violation would alsobring about a change in financial policy absent the covenant violation. For ex-ample, managers may alter their financial policies in response to changes inthe variables on which covenants are written. Alternatively, managers mayalter their financial policies in response to changes in other firm character-istics that occur contemporaneously with the violation. Therefore, the goalof our empirical strategy is to show that managers would not have alteredtheir financial policies in the same manner had the covenant violation notoccurred.

To illustrate this issue, consider the following hypothetical example in whicha credit agreement contains a covenant restricting the borrower’s debt toEBITDA ratio to remain below 3.0. Suppose then that we demonstrate thatnet debt issuance is lower for the firm when debt to EBITDA is above 3.0.In this example, we should be cautious in asserting that net debt issuance islower because the firm violates a covenant given that increases in the debtto EBITDA ratio are also likely correlated with changes in managerial prefer-ences over debt policies. For example, an increase in the debt to EBITDA ratiomay also be associated with an increase in the probability of bankruptcy or adecline in expected taxes. According to a tax-bankruptcy cost tradeoff theory,both effects would lead managers to prefer less debt. Therefore, without con-trolling for variation in the debt to EBITDA ratio and other factors associatedwith financial policy, our estimate of the impact of covenant violations ignoresthe fact that managers may have reduced net debt issuance even in the absenceof the violation.

To disentangle the effect of the covenant violation from changes in finan-cial policy that would have otherwise occurred, we estimate the impact of thecovenant violation by focusing only on discontinuous changes in financial policyoccurring at the covenant threshold. We apply this discontinuity approach byincluding as right-hand side variables a covenant violation indicator variablealong with linear, nonlinear, and step functions of the underlying variables onwhich covenants are written.8 With the inclusion of these functions, the pointestimate on the covenant violation indicator variable is identified under theassumption that managerial preferences over financial policies are not discon-tinuous exactly at the covenant threshold. This assumption is valid as long asmanagers, in the absence of financial covenants, would not have chosen the

8 For a graphical analysis of the regression discontinuity design, see the Internet Appendix.


exact same ratios and levels of the ratios as creditors to determine financialpolicy.

Both anecdotal and empirical evidence suggest that this assumption is valid.First, discussions with commercial lenders indicate that covenant restrictionsare often highly contested during the pre-origination negotiations, which sug-gests that covenants are not simply placed at the managerial chosen thresh-old.9 Second, extant research shows that interest rates are lower when loancontracts contain more covenants (Bradley and Roberts (2004)), which impliesthat covenants must be valuable for the creditor. Given the forgone interestpayments, it is unlikely that creditors place covenants at thresholds that man-agers would have used themselves in the absence of the covenants. Nonethe-less, we undertake a variety of robustness tests below to further ensure thatthis assumption is valid and that the estimated financing response is properlyidentified.

C. Isolating the Impact of Violations on Net Debt Issuance

For the empirical analysis, we construct a matrix of right-hand side vari-ables, X, consisting of 16 variables on which covenants are written. The matrixincludes 12 noninteraction (i.e., level) covenant controls and four interactionterms.10 We include these interactions given that many covenants are writtenon combinations of the underlying variables (debt to EBITDA, for example).The choice of these controls is based on the most common financial covenantsemployed in private credit agreements. Following previous empirical capitalstructure studies (e.g., Rajan and Zingales (1995)), the matrix X also includesthe lagged natural logarithm of assets, the lagged tangible to total assets ra-tio, the lagged market-to-book ratio, and a lagged indicator variable identifyingwhether the firm has an S&P credit rating.11 Given this matrix X, we estimatethe following firm fixed effects specification:

9 We are particularly grateful for discussions with Rob Ragsdale, formerly of First Union;Terri Lins, formerly of Barclays, FleetBoston, and First Union/Wachovia; Horace Zona formerlyof UBS, Toronto Dominion, and currently with First Union/Wachovia; Steven Roberts, formerlywith Toronto Dominion; and Rich Walden, Rick Gabriel, and Doug Antonossi of JP Morgan Chase& Co.

10 The level variables are: lagged book debt to assets ratio, lagged net worth to assets ratio,lagged cash to assets ratio, lagged and current EBITDA to lagged assets ratio, lagged and currentcash flow to lagged assets ratio, lagged and current net income to lagged assets ratio, and laggedand current interest expense to lagged assets ratio. The interaction terms are: lagged debt to assetsratio interacted with lagged cash flow to lagged assets ratio, lagged debt to assets ratio interactedwith lagged EBITDA to lagged assets ratio, lagged debt to assets ratio interacted with lagged networth to assets ratio, and lagged EBITDA to lagged assets ratio interacted with lagged interestexpense to lagged assets ratio.

11 Unreported analysis incorporating the median industry leverage ratio (Frank and Goyal(2003)), cash flow volatility, the marginal tax rate (Graham (1996)), and credit ratings (Kisgen(2006)) produce qualitatively similar findings. In addition, the inclusion of interacted one-digit in-dustry by quarter fixed effects does not affect our core estimates and does not raise the adjusted-R2;as a result, we do not include them in the analysis.


Di,t − Di,t−1

Ai,t−1= αi +

4∑f =1

θ f +2005q2∑

t=1996q3

δt+β0 ∗ Violationi,t

+ β1 ∗ Violationi,t−1 + � ∗ f (X i,t−1, X i,t) + ηit, (1)

where f (X) corresponds to a vector of functions of the variables on whichcovenants are written, and all other variables discussed above. While ourgeneral specification contains both lagged and contemporaneous control vari-ables, removal of all contemporaneous controls has a negligible effect on ourresults.

Column (1) in Panel A of Table III presents the estimation results from thebaseline firm fixed effects specification with only fiscal quarter and calendaryear-quarter indicator variables as controls (i.e., restricting � = 0). The re-sults show that, on average, net debt issuance falls from eight basis pointsabove the firm mean (Covenant violationt) to 62 basis points below in the quar-ter immediately after the covenant violation (Covenant violationt–1), a declineof 70 basis points. The standard errors in parentheses imply a t-statistic ofeight, even after removing firm fixed effects and accounting for within-firmcorrelation (Petersen (2009)). The specification reported in column (2) adds the12 noninteraction covenant control variables. The adjusted-R2 increases morethan threefold to over 18% relative to the baseline fixed effects specification.However, the magnitude of the covenant violation coefficient declines only mod-erately and is still statistically large. The specification reported in column (3)includes the four interaction terms, which have little impact on the adjusted-R2

or estimated covenant violation coefficient.Finally, column (4) presents the results for a kitchen sink specification in-

cluding the following controls: the 16 covenant control variables (level and in-teraction terms), higher-order polynomial terms (squared and cubic terms) foreach of the 16 covenant controls, and quintile indicator variables for each of the16 covenant controls. To be clear, the last set of controls consists of 80 (5 × 16)indicator variables, where each indicator variable equals one if the firm-quarterobservation falls in the relevant quintile of the covenant control distribution.The adjusted-R2 of the regression increases by more than three times that ofthe regression reported in column (1), suggesting that these additional controlshave significant predictive power. However, even with this extensive set of over120 covenant control variables, the covenant violation coefficient estimate islargely unaffected, remaining at –50 basis points with a t-statistic of almost7.0.

In Panel B, we present estimates from the first difference analog to the fixedeffects specification in equation (1). More precisely, the specifications reportedin Panel B examine the change in net debt issuance for a given firm as a functionof covenant violations, after controlling for changes in covenant control vari-ables. We report the first difference specification for two reasons. First, fixedeffects and first difference estimators are both consistent under standard exo-geneity assumptions (see Wooldridge (2002), pp. 284–285), and so a comparisonof the estimates is useful in assessing whether the estimation in equation (1) is


properly specified. Second, the graphical analysis in Figure 1 shows a sharp andpersistent decline in net debt issuance immediately following a covenant viola-tion, which suggests that a first difference specification may more accuratelycapture the effect of the violation on net debt issuance. As Panel B shows, theestimates from the first difference specification are similar to the fixed effectsestimates in Panel A.

Table IIICovenant Violations and Net Debt Issuance

This table presents coefficient estimates of firm fixed effects regressions (Panel A) and first differ-ence regressions (Panel B) of net debt issuance on covenant violation indicators and control vari-ables. The specifications reported in columns (2)–(4) of Panel A include lagged natural logarithmof total assets, the lagged tangible assets to total assets ratio, the lagged market-to-book ratio, anda lagged “has S&P rating” indicator as control variables. In addition, the specification in column(2) of Panel A includes the 11 covenant control variables: the lagged book debt to assets ratio, thelagged net worth to assets ratio, the lagged cash to assets ratio, the lagged and current EBITDA tolagged assets ratio, the lagged and current cash flow to lagged assets ratio, the lagged and currentnet income to lagged assets ratio, and the lagged and current interest expense to lagged assetsratio. Column (3) of Panel A includes the covenant control variables in addition to four covenantcontrol interaction variables: the lagged debt to assets ratio interacted with the lagged cash flowto lagged assets ratio, the lagged debt to assets ratio interacted with the lagged EBITDA to laggedassets ratio, the lagged debt to assets ratio interacted with the lagged net worth to assets ratio, andthe lagged EBITDA to lagged assets ratio interacted with the lagged interest expense to laggedassets ratio. Column (4) of Panel A includes all covenant control variables and covenant controlinteraction variables, these variables squared and to the third power, and five quantile indicatorvariables for each of the controls. Columns (1)–(4) of Panel B include the first differenced analogsto control variables in Panel A, with the exception of measures using debt, which are differenceslagged two quarters instead of one-quarter to avoid spurious correlations. All specifications includecalendar year-quarter indicator variables and fiscal quarter indicator variables. Standard errorsare reported in parentheses and are clustered by firm.

Panel A: Fixed Effects

Dependent Variable: Net debt issuancet/assetst−1 (Basis Points)

(1) (2) (3) (4)

Covenant violationt 8.4 3.2 2.2 3.2(8.1) (7.6) (7.7) (7.6)

Covenant violationt−1 −62.2∗∗ −50.3∗∗ −54.3∗∗ −50.3∗∗(7.8) (7.2) (7.2) (7.2)

Covenant control None Covenant Covenant control Control variables,variables control variables, covenant control variables

variables interaction control squared, controlvariables variables to the

third power, andquintile indicatorsfor each control

Number of firm-quarters 135,736 135,736 135,736 135,736Number of firms 6,381 6,381 6,381 6,381R2 0.051 0.183 0.187 0.204

(continued)


Table III—Continued

Panel B: First Differences

Dependent Variable: Change in Net debt issuancet/assetst−1 (Basis Points)

(1) (2) (3) (4)

Covenant violationt 9.2 −3.3 −2.3 2.2(11.0) (10.1) (10.1) (10.0)

Covenant violationt−1 −44.9∗∗ −60.4∗∗ −59.7∗∗ −50.3∗∗(11.2) (10.3) (10.3) (10.3)

Covenant control None Covenant Covenant control Control variables,variables control variables, covenant control variables

variables interaction control squared, controlvariables variables to the

third power, andquintile indicatorsfor each control


∗∗ Statistically distinct from zero at the 1% level.

In Table IV, we examine the long-run evolution of net debt issuance and lever-age ratios after a covenant violation. The regression specifications in columns(1) and (2) of Table IV are identical to the specifications reported in columns(1) and (4) of Table III, respectively, but for the inclusion of covenant violationindicators for eight quarters after the covenant violation. Because of this spec-ification change that requires data for eight lags, the sample for Table IV issmaller than that used in Table III.

Column (1) presents the long-run estimation results from the baseline firmfixed effects specification with only fiscal quarter and calendar year-quarter in-dicator variables as additional controls. Consistent with the results in Figure 1,the estimates show that net debt issuance drops sharply in the two quartersafter the covenant violation, and remains statistically significantly lower thanthe firm mean even eight quarters after the violation. Column (2) includesthe comprehensive set of control variables described in Table III; the short-run and long-run effects are qualitatively similar, with only slightly smallermagnitudes. Thus, the estimates presented in columns (1) and (2) indicate asharp and persistent decline in net debt issuing activity, even after includingthe additional controls for variables on which covenants are written.

The results reported in columns (3) and (4) of Table IV demonstrate thelong-run effect of the decline in net debt issuance on leverage ratios. Column(3) presents estimates from a specification including only firm, calendar year-quarter, and fiscal quarter indicator variables as controls, and shows that lever-age ratios gradually decline in response to the covenant violation—consistentwith the pattern exhibited in Panel C of Figure 1. By six quarters after theviolation, the leverage ratio is not statistically distinct from the long-run firmaverage at a meaningful confidence level. The coefficient estimates reported in


Table IVLong-Run Effect of Covenant Violations

This table presents coefficient estimates from firm fixed effects regressions of net debt issuances(columns (1) and (2)) and the leverage ratio (columns (3) and (4)) on covenant violation indicatorvariables and control variables. Column (2) contains identical control variables as column (4) ofTable III. Column (4) contains the lagged logarithm of total assets, the lagged market-to-bookratio, the lagged tangible to assets ratio, the current and lagged EBITDA to lagged assets ratio, thecurrent and lagged cash flow to lagged assets ratio, the current and lagged net income to laggedassets ratio, and a “has S&P rating” indicator variable. All specifications include calendar year-quarter indicator variables and fiscal quarter indicator variables. Standard errors are reported inparentheses and are clustered by firm.

Dependent Variable: Dependent Variable:Net Debt Issuancet/Assetst−1 Leverage Ratio

(Basis Points) (Basis Points)

(1) (2) (3) (4)

Covenant violationt 19.6∗ 11.3 290.7∗∗ 214.4∗∗(8.7) (8.3) (28.8) (28.4)

Covenant violationt−1 −32.8∗∗ −28.8∗∗ 222.9∗∗ 157.7∗∗(8.8) (8.3) (24.9) (24.6)



Covenant violationt−4 −26.6∗∗ −22.6∗∗ 56.3∗ 43.4∗(9.0) (8.5) (22.3) (21.6)


Covenant violationt−6 −27.1∗∗ −25.5∗∗ 40.7 27.1(9.1) (8.6) (21.6) (21.0)

Covenant violationt−7 −17.9∗ −17.2∗ 9.4 2.1(8.7) (8.2) (23.0) (22.4)

Covenant violationt−8 −30.6∗∗ −33.6∗∗ −22.9 −21.8(9.1) (8.6) (27.8) (27.0)

Control variables None All covenant control None Leverage controlvariables from variablesTable III, column (4) (listed above)


∗,∗∗ Statistically distinct from zero at the 5% and 1% level, respectively.

column (4) are from a specification that includes controls commonly used in thecapital structure literature (lagged natural logarithm of assets, lagged assettangibility, lagged market-to-book, lagged “has S&P rating” indicator, and cur-rent and lagged EBITDA, cash flow, and net income scaled by lagged assets).The results are similar.12

12 We implicitly account for the dynamic properties of leverage by allowing for serial correlationin the within-firm error structure (Lemmon, Roberts, and Zender (2008)). Further, unreportedanalysis of leverage defined by subtracting cash from debt produces qualitatively similar results.


To gauge the economic significance of these results, we conduct two exer-cises. First, we examine how a covenant violation moves a firm in the net debtissuance and leverage distributions. Given the inclusion of firm fixed effects inthe regression analysis that produces the point estimates, we use the within-firm distributions (i.e., the distribution after removing firm fixed effects) as abenchmark. The 70 basis point drop reported in column (1) of Table III, Panel Amoves a firm from the 65th to the 35th percentile of the distribution, and the54 basis point drop in column (4) moves a firm from the 65th to the 40th per-centile of the distribution—a full quartile of the conditional distribution. Forleverage, the 314 (290.7 – (–22.9)) basis point drop reported in column (3) ofTable IV moves a firm from the 72nd to the 45th percentile of the distribution,and the 236 basis point drop in column (4) moves a firm from the 69th to the45th percentile of the distribution—again a full quartile.

Our second exercise is to compare the effect of a violation to the marginaleffect of traditional determinants of financial policy. To do so, we first estimatethe effect of covenant violations on net debt issuances and leverage ratios usingonly traditional control variables found in the capital structure literature (e.g.,Rajan and Zingales (1995), Baker and Wurgler (2002), and Frank and Goyal(2005)), as well as firm and period fixed effects. Panel A of Table V presentsthe parameter estimates and within-firm standard deviations of each right-hand side variable. The last column presents the product of the parameter andtwo times the standard deviation for the purpose of our comparisons. As illus-trated by the table, the marginal impact of a covenant violation is substantiallylarger than every other control variable, even after allowing for relatively largechanges in the underlying variable.

To perform an analogous comparison for leverage, we turn to a more gen-eral finite distributed lag model. More specifically, we lag each control variableeight periods in the regression specification. To ease the interpretation of ourresults, we estimate the model in first difference form. We then compute thelong-run multiplier for each control variable as the sum of the eight estimatedslope coefficients. This measure estimates the impact of a one-unit change tothe control variable on the long-run firm-specific leverage ratio. As before, wemultiply each control variables’ estimate by twice the corresponding standarddeviation in order to examine how a large change in the corresponding variableaffects the firm’s long-run leverage ratio.

Panel B of Table V presents the results, which show that the long-run impactof a covenant violation on firms’ leverage ratios is greater than the long-runimpact of most previously identified determinants. Specifically, from the quar-ter of the violation to the eight quarters after the violation, firms reduce theirleverage ratio by an average of 235 basis points. The next most significant pre-dictor is firm profitability. A two-standard deviation increase in this variableleads to a 223 basis point decline in the firm’s long-run leverage ratio. These re-sults demonstrate that the decline in the leverage ratio that follows a covenantviolation is large relative to the impact of other variables previously examinedin the capital structure literature. However, we emphasize that the comparisonpresented in Panel B of Table V is focused on the long-run effects. The short-run


or immediate effect on leverage of most previously identified determinants issignificantly larger than that of a covenant violation. Covenant violations areassociated with a gradual but persistent effect on leverage ratios because ofthe persistent decline in net debt issuances, whereas shocks to most previouslyidentified control variables appear to have an immediate effect that tends todecay over time (e.g., Kayhan and Titman (2007)).

Table VEconomic Magnitudes

Panel A presents a comparison of the economic magnitude of a covenant violation and other vari-ables on net debt issuance. Column (1) presents coefficient estimates of firm fixed effects regressionsof net debt issuance on covenant violations and controls. The specification includes calendar year-quarter indicator variables and fiscal quarter indicator variables. Standard errors are reported inparentheses and are clustered by firm. Column (2) reports the within-firm standard deviation ofthe right-hand side variables, and column (3) reports the effect of a two standard deviation changein the right-hand side variable on net debt issuance. Panel B presents a comparison of the magni-tude effects of a covenant violation and other variables on the long-run leverage ratio. Column (1)presents the long-run effect of each variable on the leverage ratio. The basis for the long-run effectis a first difference regression that includes eight lags for each differenced variable in additionto calendar year-quarter indicator variables and fiscal quarter indicator variables. For all of thevariables except for the covenant violation indicator variable, the long-run multiplier is calculatedby adding the coefficient estimates on the eight lags of the differenced variable in question. Forthe covenant violation indicator variable, the long-run multiplier is the coefficient estimate on theeight-quarter lag term minus the coefficient estimate on the contemporaneous term. Column (2)reports the within-firm standard deviation of the right-hand side variables, and column (3) re-ports the effect of a two standard deviation change in the right-hand side variable on the long-runleverage ratio.

Panel A: Net Debt Issuances

Dependent Variable: Net Debt Issuancet/Assetst−1 (Basis Points)

(2) (3)(1) Within-Firm Estimate ∗ 2 SD

Coefficient Standard Deviation Increase in RHSEstimates of RHS Variable Variable

Covenant violationt−1 −66.2∗∗(7.6)

Ln(Assetst−1) −51.6∗∗ 0.535 −55.2(3.6)

(EBITDA/assets)t−1 −455.5∗∗ 0.036 −32.8(45.5)

Market-to-bookt−1 9.3∗∗ 1.191 22.2(1.1)

(Tangible assets/assets)t−1 154.4∗∗ 0.071 21.9(24.5)

Industry median leveraget−1 −531.2∗∗ 0.023 −24.4(59.4)

Number of firm-quarters 135,736Number of firms 6,381R2 0.107

(continued)


Table V—Continued

Panel B: Leverage Ratios

Dependent Variable: Leverage Ratio (Basis Points)

(2) (3)(1) Within-Firm Estimate ∗ 2 SD

Long-Run Standard Deviation Increase in RHSMultiplier of RHS Variable Variable

Covenant violation −235.2∗∗Ln(Assets) −36.0 0.235 −16.9(EBITDA/assets) −3,096.9∗∗ 0.036 −223.0Market-to-book −62.1∗∗ 0.830 −103.1(Tangible assets/assets) 679.1∗ 0.042 57.0Industry median leverage −491.6 0.010 −9.8Number of firm-quarters 73,411Number of firms 5,118R2 0.064

In Panel A: ∗,∗∗ statistically distinct from zero at the 5% and 1% level, respectively.In Panel B: ∗,∗∗ estimate of long-run effect statistically distinct from zero at the 5% and 1% level,respectively.

D. Robustness

D.1. Managerial Rebalancing of Leverage Ratios

In this section, we examine whether the estimated effect of the covenant vio-lation on net debt issuance simply reflects managerial rebalancing of leverageratios. Previous research suggests that managers dynamically rebalance theirleverage ratios (Leary and Roberts (2005)) and many managers explicitly re-port having a target range for the debt to equity ratio (Graham and Harvey(2001)). Given that covenant violations occur when leverage ratios are high,the concern is that managers are reacting to the higher leverage ratio, andthere is no direct effect of the violation itself. The previous results in Table IIIlargely mitigate this concern by showing that the magnitude of the effect ofcovenant violations on net debt issuance is robust to both parametric andnonparametric controls for the lagged leverage ratio. Nonetheless, we inves-tigate this issue explicitly here because of its importance for our identificationstrategy.

We first examine the change in net debt issuance for covenant violators ver-sus nonviolators across the leverage distribution. In Panel A of Table VI, thesample is split into quartiles based on the level of the leverage ratio in periodt−1. Importantly, the quartiles are constructed using the entire sample con-taining both violators and nonviolators. The first column shows a rebalancingeffect among nonviolators, albeit a nonmonotonic effect. Firms in higher laggedleverage quartiles have smaller increases in net debt issuance, which is con-sistent with the rebalancing evidence in previous studies. Column (2) showsthat the net debt issuance of covenant violators is lower in every quartile of the


Table VICovenant Violations versus Leverage Rebalancing

This table presents evidence on covenant violations and managerial leverage rebalancing. Thesample includes firms that have an average book leverage ratio of 0.05 or greater for the sample.In Panel A, firm-quarter observations at time t are separated into quartiles based on the leverageratio at t−1. In Panel B, firm-quarter observations at time t are separated into quartiles basedon the debt to EBITDA ratio at t−1, and observations with negative EBITDA are excluded. Foreach quartile, the mean net debt issuance scaled by lagged assets at time t is reported for firmsthat violate and do not violate a covenant at time t−1. Panel C presents fixed effects regressioncoefficient estimates where the specification in column (1) includes an interaction between thelagged covenant violation indicator variable and the lagged leverage ratio and the specification incolumn (2) includes an interaction between the lagged covenant violation indicator variable andthe lagged debt to EBITDA ratio. Standard errors are reported in parentheses and are clusteredby firm.

Panel A: Leverage Ratio

Mean Net Debt Issuance Scaled by Lagged Assets (Basis Points)t

No Covenant Violationt−1 Covenant Violationt−1

Leverage Quartile 1 106 99Leverage Quartile 2 56 14++Leverage Quartile 3 39 −16++Leverage Quartile 4 69 −27++

Panel B: Debt to EBITDA Ratio

Mean Net Debt Issuance Scaled by Lagged Assets (Basis Points)t

No Covenant Violationt−1 Covenant Violationt−1

Debt to EBITDA Quartile 1 91 57Debt to EBITDA Quartile 2 40 −30++Debt to EBITDA Quartile 3 25 −23++Debt to EBITDA Quartile 4 32 −20++

Panel C: Fixed Effects Regressions

Dependent Variable: Net Debt Issuancet/Assetst−1 (Basis Points)

(1) (2)

Leverage ratiot−1 −500.0∗∗(22.4)

Leverage ratiot−1 ∗ Violationt−1 −135.1∗∗(21.6)

Debt to EBITDAt−1 −19.5∗∗(1.2)

Debt to EBITDAt−1 ∗ Violationt−1 −4.0∗(1.6)

Number of firm-quarters 104,383 78,643Number of firms 4,765 4,272R2 0.116 0.105

++ Statistically distinct from “no covenant violation” at the 1% level.∗,∗∗ Statistically distinct from zero at the 5% and 1% level, respectively.


distribution of lagged leverage ratios. In fact, covenant violators in the secondquartile have an average net debt issuance that is lower than that of nonvio-lators in the highest leverage quartile, a difference that is statistically distinctfrom zero at the 5% level. If managerial rebalancing is the only effect, then it isunlikely that violators in lower leverage quartiles would be reducing net debtissuance by more than nonviolators in higher leverage quartiles.

In Panel B, the sample is split into quartiles based on the debt to EBITDAratio. Column (2) shows that violators have lower net debt issuance relativeto nonviolators in every quartile of the debt to EBITDA ratio. As in Panel A,covenant violators with relatively low debt to EBITDA ratios have lower netdebt issuance than nonviolators with high debt to EBITDA ratios. For example,violators in the second quartile have net debt issuance of –30 basis points whilenonviolators in the fourth quartile have net debt issuance of +32 basis points, adifference that is statistically distinct from zero at the 1% level. If one interpretsthe debt to EBITDA ratio as a measure of financial health, the results in PanelC suggest that financially healthy violators reduce net debt issuance by morethan financially unhealthy nonviolators, implying that financial distress alone(i.e., in the absence of a covenant violation) is not responsible for the reductionin net debt issuance that we observe.

Finally, Panel C examines the rebalancing alternative in a regression con-text. The specification in column (1) is identical to the specification reportedin column (1) of Table III, except for the inclusion of the lagged leverage ra-tio and the interaction of the lagged leverage ratio with the lagged covenantviolation indicator variable. As the coefficient estimate on the lagged leverageratio indicates, firms reduce net debt issuance when leverage ratios are high.This finding coincides with the rebalancing found in previous empirical capitalstructure studies. However, the coefficient estimate on the interaction term in-dicates that covenant violators reduce net debt issuance by significantly morethan nonviolators in response to high leverage ratios. In fact, net debt issuancedecreases by an additional 27% relative to the baseline rebalancing effect im-plied by the lagged leverage coefficient.

Column (2) reports the results from a similar specification with the debtto EBITDA ratio. These results reinforce the interpretation that rebalancingalone is not responsible for our results. Firms with higher debt to EBITDA ratiosreduce net debt issuance, but the effect among violators is significantly stronger.The response of net debt issuance by violators to higher debt to EBITDA ratiosis 20% stronger than the response by nonviolators.13

The coefficient estimates in Panel C also provide a useful interpretation ofmagnitudes. Relative to managerial rebalancing, the estimates suggest that acovenant violation is associated with a reduction in net debt issuance that is20 to 30% larger in response to higher debt levels. That is, covenant violations,

13 The differences in coefficient estimates across the leverage and debt to EBITDA ratio speci-fications are due to scale—normalizing by the relative standard deviations leads to economicallysimilar effects.


on average, lead to a reduction in net debt issuance that is significantly largerthan we would otherwise observe.14

D.2. Regression Discontinuity Using Dealscan Sample

In this section, we isolate the analysis to a sample of loans for which weknow the covenant thresholds, as well as any changes (or “buildup”) in thosethresholds. Such an analysis alleviates two potential concerns associated withthe preceding results: (1) the exact covenant threshold is unknown and (2) weobserve only reported covenant violations. The analysis and sample are similarto those found in Chava and Roberts (2008). To avoid any redundancy andmanage the length of our study, we purposely keep the discussion of the dataand methodology brief, referring the reader to their study for further details.

The sample consists of all loan-quarter observations satisfying the followingcriteria. First, the loan and borrower must lie in the intersection of the Dealscanand Compustat databases to ensure the availability of loan and accounting in-formation. Second, the loan must contain either a current ratio or net worthcovenant to ensure an accurate measurement of the relevant accounting vari-able.15 The final sample is a panel of firm-quarter observations in which eachobservation either is or is not in violation of a covenant. To determine whethera firm is or is not in violation, we compare the firm’s actual accounting measure(i.e., current ratio or net worth) to the covenant threshold implied by the termsof the contract.

Our empirical strategy in this section can be viewed as a refinement of thatdiscussed above in Section III.C, because this subsample allows us to incorpo-rate the precise distance to the covenant threshold into our regression specifica-tion. Formally, our empirical strategy in this section is a regression discontinu-ity design in which the function mapping the distance between the underlyingaccounting variable and the covenant threshold is discontinuous. Specifically,our treatment variable, Violation, is defined as

Violationit ={

1 if zit − z0it < 0

0 otherwise,(2)

where z is the observed current ratio (or net worth), z0 is the covenant threshold,and i and t index firms and quarters, respectively.

Our empirical model for this section is similar to that in the previous section,

Dit − Dit−1

Ait−1= αi +

4∑f =1

θ f +2004q1∑1994q1

δt + β0Violationit−1 + β ′1 X it−1 + ηit, (3)

14 Results available in the Internet Appendix also show that violators across the change inEBITDA/book assets ratio distribution experience lower net debt issuance than non-violators.

15 Covenants restricting the debt to EBITDA ratio, for example, create a problem when tryingto measure this ratio with Compustat accounting data since “debt” can refer to any component ofa firm’s debt structure including: long-term, short-term, senior, junior, secured, total, funded, etc.


where all variables are defined above. The parameter of interest is β0, whichrepresents the impact of a covenant violation on firm i’s net debt issuing ac-tivity. As discussed earlier, the appeal of the regression discontinuity approachis that the effect of the violation is consistently estimated under very mildassumptions. Specifically, the identifying assumption is that the error term,ηit, does not exhibit precisely the same discontinuity as the violation (Hahn,Todd, and Van der Klaauw (2001)). See Rauh (2006) for another application incorporate finance.

The estimation results, available in the Internet Appendix,16 reveal a declinein net debt issuance of 60 basis points following the violation—almost identi-cal to that found above for the whole sample. Importantly, this result is robustto the inclusion of traditional determinants of capital structure decisions, aswell as smooth functions of the underlying distance to the covenant thresh-old. Additionally, estimation of equation (3) on the subsample of firm-quarterobservations that are “close” to the point of discontinuity reveals a similar 60basis point decline in net debt issuances following the violation. Overall, thesimilarity of our results in the Dealscan sample with those found in the pri-mary sample mitigates concerns over endogeneity of the covenant thresholdand self-reported violations.

D.3. Avoiding Covenant Violations

One consideration associated with covenant violations is the impact that theymight have on the ex ante actions of managers, who may attempt to avoid vi-olating a covenant through accounting manipulation or other activities (e.g.,Dichev and Skinner (2002) and Dyreng (2007)). We consider this issue by ex-amining the impact of incorporating into our regressions measures of abnormalaccruals, which, despite being somewhat noisy (Dechow, Sloan, and Sweeney(1995)), have “the potential to reveal subtle manipulation strategies related torevenue and expense recognition” (DeFond and Jiambalvo (1994, p. 149)). Weexamine several different measures including: abnormal total accruals (De-Fond and Jiambalvo (1994)), abnormal working capital accruals (DeFond andJiambalvo (1994)), and abnormal current accruals (Teoh, Welch, and Wong(1998) and Bharath, Sunder, and Sunder (2008)). The results, not reported,reveal a marginally significant correlation with financial policy but, more im-portantly, reveal nearly identical estimates of the impact of covenant violationson net debt issuance.

Beyond ensuring the robustness of our inferences, these results are reassur-ing for two additional reasons. First, the notion that managers can consistentlyfool commercial bank lenders through accounting manipulation is questionable.CFOs are required to submit periodic covenant compliance reports that discussin great detail the computation of and adherence to each financial covenant.Additionally, creditors have significant experience in originating and monitor-ing loans and are well aware of possible accounting manipulations. Indeed,most every loan contract spells out in detail the precise accounting conventions

16 An Internet Appendix for this article is online in the “Supplements and Datasets” section athttp://www.afajof.org/supplements.asp.


to be used in the computation of the covenants’ accounting ratios (Taylor andSansone (2007)). Finally, the lending process is a repeated game, which lessensthe attractiveness of any borrower deception because of the risk to future fi-nancing needs.

Second, the survey results of Graham, Harvey, and Rajgopal (2005) suggestthat managers are more likely to take real actions, such as cutting investment,to meet these goals, as opposed to manipulating accounting statements. In ourcontext, the fact that some managers may cut investment to avoid a violationwill tend to work against finding evidence of a significant effect of covenant vio-lations on net debt issuing activity. The reason is as follows: Managers are morelikely to take action to avoid a violation when violating is costly. Consequently,the observed violations are precisely those situations in which managers believethat it is relatively less costly to violate. For example, it is less costly for man-agers with poor investment opportunities to cut investment in order to avoidthe violation. Therefore, observed covenant violators have better investmentopportunities, on average, than the unobserved sample of true violators. Giventhe better investment opportunities, banks are less likely to increase the costof credit or limit access to credit. Thus, the possibility that some managers takeex ante actions to avoid violations suggests that our estimates of the impact ofcovenant violations on financial policy may be conservative.

IV. How Do Violations Affect Financial Policy?

A. Initial versus Subsequent Violations

Covenant violations occur in clusters. For example, in our sample, a firm isalmost 20 percentage points more likely to violate a financial covenant if itviolated a covenant in the previous four quarters. As a result, it is importantto take into account how the sequence of violations affects security issuancedecisions. To do so, we estimate the regression

(Dit − Dit−1

Ait−1

)=

4∑f =1

θ f +2005q2∑

t=1996q3

δt +8∑

j=0

β j I (InitialViolationit+ j )

+8∑

j=0

γ j I (SubViolationit+ j ) + f (X it, X it−1) + ηit, (4)

which separates the effect of initial versus subsequent violations on the changein net debt issuance policy. We focus on a first difference specification to easethe interpretation of our results, though results from a firm fixed effects spec-ification produce identical inferences. The estimate for β1 captures the effectof an initial covenant violation in the previous quarter on the change in netdebt issuance, and the estimate for γ 1 captures the incremental additional ef-fect of a subsequent violation in the previous quarter on net debt issuance.An initial violation is defined to be a violation for a firm that has not violateda covenant in the previous four quarters, although lengthening this windowto the previous eight quarters does not affect the results. All other violations


are considered subsequent violations. We include eight lags of both initial andsubsequent violations to capture the full time series of the effect. The matrixof control variables X includes all covenant control variables in column (3) ofTable III, Panel B.

Column (1) of Table VII presents estimates of the effect of initial (“New”)violations on the change in net debt issuance (i.e., with the parameter vectorγ restricted to zero). The estimates show that initial violations have a strongeffect on the change in net debt issuances: In the first-quarter after the initialcovenant violation, net debt issuance is 81 basis points lower than the previ-ous quarter. In the second quarter after the initial violation, net debt issuancedeclines by an additional 66 basis points. The cumulative effect of initial viola-tions is almost 150 basis points in the two quarters after the initial violation.The effect of initial violations is quickly realized, as is evident from the smallereffects of the third through eighth lags.

Column (2) presents estimates from the full specification including subse-quent violations. Unlike the initial covenant violations, subsequent violationshave a much smaller incremental effect on net debt issuances. The largest ef-fect occurs in the quarter immediately after a subsequent violation, where netdebt issuance declines by almost 30 basis points. However, this estimate is notstatistically significant at a reasonable confidence level. The effect of initialviolations on net debt issuance in the two quarters after the violation remainsstatistically significant and economically large.

Because there is a large decline in net debt issuance in the two quartersafter an initial violation, we examine whether subsequent violations have anincremental effect on net debt issuance if they occur during these two quarters.Column (3) shows that the coefficient estimate on new violationt–1 ∗ subsequentviolationt is –44.7, which implies that net debt issuance falls by an additional44.7 basis points in the quarter after an initial covenant violation if the borroweragain violates a covenant. However, the estimate is not statistically significantat a reasonable confidence level. Subsequent violations in the second-quarterafter the violation also do not have a statistically significant incremental effecton net debt issuance.

Overall, the estimates in Table VII demonstrate that the sharpest declinein net debt issuance occurs after the initial violation as opposed to subsequentviolations. This result suggests that there are significant changes in financialpolicy after creditors initially obtain acceleration and termination rights, a find-ing that is consistent with models by Aghion and Bolton (1992) and Dewatripontand Tirole (1994). These results support the interpretation that the provisionof control rights leads to a change in net debt issuance policy that would nothave otherwise occurred.17

17 In unreported analysis, we perform a similar examination of net equity issuances. The resultsreveal no significant differences in the response of net equity issuances to initial versus subsequentviolations, suggesting that the results in Figure 1 are not masking heterogeneity in the equity policyresponse to covenant violations.


Table VIIInitial versus Subsequent Covenant Violations

This table presents coefficient estimates from first difference regressions of the change in netdebt issuances on new versus subsequent covenant violation indicator variables. A new violation isdefined to be a violation in which there is no violation by the same firm in the previous four quarters.All other violations are subsequent violations. All specifications include calendar year-quarterindicator variables and fiscal quarter indicator variables, and all control variables in Column (3)of Table III, Panel B. Standard errors are reported in parentheses and are clustered by firm.

Dependent Variable: Change in [Net Debt Issuancet/Assetst−1 (Basis Points)]

(1) (2) (3)

New violationt 17.1 16.2 New violationt 17.1(15.9) (15.9) (15.9)

New violationt−1 −80.7∗∗ −81.1∗∗ New violationt−1 −64.0∗∗(16.9) (18.0) (21.7)

New violationt−2 −66.4∗∗ −56.4∗∗ New violationt−1 ∗ Subsequent violationt −44.7(16.1) (16.6) (34.5)

New violationt−3 0.6 8.3 New violationt−2 −68.3∗∗(15.6) (16.1) (20.3)

New violationt−4 −19.0 −12.2 New violationt−2 ∗ Subsequent violationt−1 −27.7(15.9) (16.6) (36.8)

New violationt−5 −42.5∗∗ −30.1 New violationt−2 ∗ Subsequent violationt 45.7(16.4) (17.1) (40.4)

New violationt−6 −10.0 0.4 New violationt−3 0.6(16.0) (17.1) (15.6)

New violationt−7 −12.4 −3.8 New violationt−4 −19.0(15.8) (17.3) (15.9)

New violationt−8 5.0 6.5 New violationt−5 −42.5∗∗(16.5) (17.7) (16.4)

Subsequent violationt −1.8 New violationt−6 −10.1(15.0) (16.0)

Subsequent violationt−1 −28.8 New violationt−7 −12.4(17.9) (15.8)

Subsequent violationt−2 −1.5 New violationt−8 4.9(17.9) (16.5)

Subsequent violationt−3 −4.8(17.8)



Subsequent violationt−6 1.2(17.9)

Subsequent violationt−7 18.3(19.4)


Number of firm-quarters 88,485 88,485 88,485Number of firms 5,609 5,609 5,609R2 0.131 0.131 0.131



B. Cross-sectional Variation in the Financing Responseto Covenant Violations

In this section, we explore cross-sectional variation in the effect of viola-tions. Figure 1 and Table VII demonstrate that the primary drop in net debtissuance occurs in the two quarters immediately after an initial covenant vio-lation. We therefore focus on the drop in these two quarters when examiningcross-sectional variation, though using only the first-quarter following the vio-lation produces qualitatively similar results. More specifically, the analysis inTable VIII limits the sample to the 1,593 initial violations in our sample andexamines what firm characteristics affect the magnitude of the drop in net debtissuance in the two quarters after the violation. The general specification takesthe following form.

Table VIIICross-sectional Heterogeneity of the Effect of Covenant Violation

This table examines the cross-sectional heterogeneity of the effect of a covenant violation on netdebt issuance. The sample is isolated to 1,593 observations where firms experience a new covenantviolation. The dependent variable is the change in net debt issuance from the quarter of a newviolation to two quarters after a new violation. A new violation is defined to be a violation in whichthere is no violation by the same firm in the previous four quarters. The independent variables aremeasures of firm characteristics at the time of the violation. Standard errors are clustered by firm.

Dependent Variable:Change in [Net Debt Issuancet/Assetst−1 (Basis Points)] in Two Quarters after New Violation

(1) (2) (3) (4)

Constant −126.1∗∗ −106.2∗∗ −47.8 15.6(15.3) (18.2) (64.3) (70.2)

Subsequent violationt−1 −58.9 −52.2 −47.9(32.5) (32.3) (32.4)

Cash/assetst−2 −49.4 −52.9(105.4) (105.1)

Leverage ratiot−2 −440.4∗∗ −483.1∗∗(87.9) (89.0)

Market-to-bookt−2 29.0∗ 28.1∗(13.2) (13.2)

EBITDA/assetst−2 212.8 259.0(316.9) (317.8)

(Tangible assets/assets)t−2 105.9 106.2(73.0) (73.0)

Ln(assets)t−2 −0.2 −14.7(9.4) (11.6)

Has S&P credit ratingt−2 114.5∗(53.1)

Number of observations 1,593 1,593 1,593 1,593Number of firms 1,332 1,332 1,332 1,332R2 0.000 0.002 0.026 0.028



(Dit − Dit−1

Ait−1

)−

(Dit−2 − Dit−3

Ait−3

)= α + βX it−1 + γ X it−2 + ηit. (5)

This specification provides estimates of how firm characteristics in the quartersof and immediately after the violation lead to differential reductions in net debtissuance. Our primary goal is to examine whether the provision of terminationand acceleration rights to existing creditors after a violation affects borrowersdifferentially based on their relative bargaining power. As a result, the matrix Xcontains information on subsequent violations, financial conditions, and accessto alternative sources of capital.

Column (1) shows that firms experience an average reduction in net debtissuance of 126 basis points in the two quarters after the covenant violation,which is consistent with Panel A of Figure 1 and Table VII. In column (2), we ex-plore whether the drop in net debt issuance is larger if a violator subsequentlyviolates a covenant in the quarter after the violation. Consistent with the es-timates in Table VII, we find that firms that subsequently violate a covenantexperience an additional drop of 59 basis points, but this estimate is statisticallysignificant only at the 7% level.

In column (3), we examine how financial condition at the time of the viola-tion influences the reduction in net debt issuance after the violation. Firmswith high leverage ratios experience sharper declines in net debt issuance. Theestimate implies that a one-standard deviation increase in the leverage ratio(21%) in the quarter of the violation leads to an additional 92 basis point reduc-tion in net debt issuance, a 75% reduction relative to the mean. Firms with highmarket-to-book ratios experience a drop in net debt issuance that is somewhatsmaller, though still statistically significant. A one-standard deviation increasein the market-to-book ratio (1.3) leads to a change in net debt issuances that is36 basis points higher, 30% relative to the mean reduction. In column (4), weinclude an indicator for whether the firm has a credit rating from S&P. We findthat on average rated firms experience a reduction in net debt issuance that is115 basis points smaller than unrated firms, which is almost 100% of the meaneffect.

The results in columns (3) and (4) demonstrate that covenant violations leadto a larger reduction in net debt issuances for firms with reduced debt capacity,lower market valuations, and limited access to rated debt instruments. Thesefindings suggest that covenant violations have a strong effect on net debt is-suance when the firm cannot easily access alternative sources of capital onmore favorable terms.18 Thus, violations by firms with limited outside optionstend to be more severe in terms of their consequences for financial policy.

C. The Actions of Creditors

Finally, we provide additional evidence on the underlying mechanismsthrough which violations affect financial policy by examining the 10-Q and

18 In unreported analysis, we also find that firms’ financial policy response to covenant violationsis amplified when competitors in the same industry are also experiencing violations, though theeffect is statistically weak.


10-K filings of a random sample of covenant violators. An examination of thefilings is useful given that some firms provide detailed explanations of the out-come of the covenant violation. These explanations provide unique insight intohow creditors use their acceleration and termination rights. The drawback ofthe explanations is that firms voluntarily choose the level of detail to report.The SEC does not provide strict guidelines for the reporting of covenant vio-lations, other than requiring the firm to report the violation and its effect onthe business if material. Therefore, the fact that a firm does not explicitly statethat a creditor took some action does not imply that the creditor in fact took noaction.

To uncover trends in violation outcomes, we examine the SEC filings of a ran-dom sample of 500 initial covenant violators. Panel A of Table IX examines thefraction of firms reporting different outcomes (column (1)) and the cumulativechange in net debt issuance in the two quarters after the violation conditionalon the outcome (column (2)). As column (1) shows, in 32% of the cases, the firmreports in its SEC filings that existing creditors take some action in responseto the violation. The most common action is a reduction in the size of the creditfacility (24%). Creditors also increase the interest spread (15%) and requireadditional collateral (7%). In 63% of the violations, the firm reports that theexisting creditors granted a waiver for the violation, but no additional actionis reported. However, as aforementioned, SEC regulations do not require firmsto detail the exact terms of the waiver, so we cannot be sure that no creditoraction was taken when there is no reported action.

The last row of column (1) reveals that only 4% of borrowers report termi-nating the credit facility in the two quarters after the covenant violation. Inother words, despite the unfavorable terms offered by existing creditors, veryfew firms choose to terminate the existing credit facility. This result suggeststhat most firms are unable to obtain more favorable financing from alternativesources after a covenant violation, which makes financial decisions particularlysensitive to the willingness of existing creditors to supply credit.19

Column (2) shows that the drop in net debt issuance is significantly larger inthe two quarters after an initial violation for firms that report some creditor ac-tion. Specifically, firms that report some action by existing creditors experiencea reduction in net debt issuance equal to 418 basis points—or 4.18% of laggedassets—in just two quarters. Firms that report receiving waivers experiencean increase in net debt issuance of 29 basis points, which is statistically signif-icantly distinct from firms that report some creditor action. Finally, net debtissuance for borrowers that terminate their existing credit facility decreases by190 basis points, but this statistic should be interpreted cautiously given thatthere are only 22 borrowers in this category.

In Panel B, we examine cross-sectional variation in the propensity for credi-tors to take action based on firm characteristics in the quarter of and after theviolation. We do so by estimating a probit regression via maximum likelihood,where the dependent variable is equal to one if the existing creditor takes an

19 In four cases, the creditor terminated the agreement within two quarters after the initialviolation. In all four cases, the creditor first took some action (reducing facility and/or increasingthe interest spread), and then requested that the borrower find a new lender.


Table IXThe Response of Creditors to Covenant Violations

This table presents evidence from SEC 10-K and 10-Q filings on how creditors respond to newcovenant violations. A new violation is defined to be a violation in which there is no violation by thesame firm in the previous four quarters. The data reported in this table are for a random sampleof 500 covenant violators for whom we examine the filings in the quarter of, the quarter after, andtwo quarters after the violation. The change in net debt issuance is the change from the quarter ofthe violation to two quarters after the violation. Panel A examines the outcomes of the violations,and Panel B presents marginal effect estimates from probit specifications relating the probabilityof creditors taking action to firm characteristics at the time of the violation. We note that borrowersare not required to report any actions taken by creditors.

Panel A: Response to Covenant Violations

(2)(1) Change in Net Debt Issuance,

Fraction Conditional on Outcome

Creditor takes some action 0.322 −418Reduction in size of credit facility 0.238 −475Interest rate increased 0.148 −401Additional collateral required 0.072 −436

Creditor grants waiver, no action reported 0.626 29++Borrower terminates credit agreement 0.044 −190

Panel B: Probit Estimates

Dependent Variable: Creditor Takes Some Action Following Violation {0,1}

(1) (2) (3)

Subsequent violationt−1 0.15∗ 0.12∗∗ 0.12∗∗(0.04) (0.05) (0.05)

Cash/assetst−2 −0.43∗∗ −0.44∗∗(0.16) (0.16)

Leverage ratiot−2 0.43∗∗ 0.47∗∗(0.12) (0.12)

Market-to-bookt−2 −0.03 −0.02(0.02) (0.02)

EBITDA/assetst−2 −1.09∗ −1.15∗(0.49) (0.49)

(Tangible assets/assets)t−2 −0.02 −0.02(0.10) (0.10)

Ln(assets)t−2 0.00 0.03(0.01) (0.02)

Has S&P credit ratingt−2 −0.16∗(0.06)

Number of observations 474 474 474R2 0.018 0.074 0.084

++ Statistically distinct from “creditor takes some action” at the 1% level.∗,∗∗ Statistically distinct from zero at the 5% and 1% level, respectively.

explicit action and zero otherwise. The estimated marginal effects in column(3) show that firms with high cash balances, low leverage ratios, high cash flow,and an S&P credit rating at the time of the violation are less likely to experiencean unfavorable creditor action. These estimates suggest that existing creditors


are less likely to reduce credit, increase interest spreads, or require additionalcollateral for firms with additional debt capacity and greater access to alterna-tive sources of capital.

In unreported analysis, we also investigate the long-run effects of a covenantviolation in our random sample.20 We find that among firms that report cred-itor action following covenant violations, net debt issuance drops by almost140 basis points as a fraction of lagged assets in the two quarters following theviolation. Additionally, the decline persists over the subsequent 2 years, whichresults in a 270 basis point decline in leverage ratios over the 2 years followingthe violation. In contrast, the drop in net debt issuance and leverage for vio-lators that do not report creditor action is much smaller and not statisticallydistinct from zero at a meaningful confidence level.

Overall, the results in this subsection provide important insights into themechanism through which violations affect financial policy. A large fraction offirms explain in their SEC filings that existing creditors react to the covenant vi-olation by reducing the size of the credit facility, increasing the interest spread,and requiring additional collateral. In addition, very few borrowers terminatetheir existing agreement, which suggests that borrowers are unable to obtainmore favorable terms from other lenders. Firms with additional debt capacityand greater access to alternative sources of capital are less likely to experiencean unfavorable creditor action. Finally, the long-run effect of the violation onfinancial policy is significantly stronger when firms report creditors taking ac-tion in response to the violation. Taken together, these findings suggest thatthe provision of rights to creditors has a large effect on net debt issuance pol-icy because firms are particularly susceptible to changes in the willingness tosupply funds by existing creditors after a violation.21

V. Conclusion

This study shows that incentive conflicts play an important role in shapingcorporate financial policy. Specifically, we show that financial covenant viola-tions lead to large and persistent declines in net debt issuing activity by provid-ing creditors with limited rights to influence financial policy via changes to theterms of the credit agreement. Consequently, firms’ leverage ratios also exhibita significant decline relative to the typical within-firm variation in leverage ra-tios; however, covenant violations have a limited impact on the cross-sectionaldistribution of leverage ratios. Further, we show that the financing response tocovenant violations is stronger when (1) existing creditors take various actions(e.g., increase interest rates, reduce allowable borrowings, etc.) to moderate thesupply of credit, and (2) borrowers’ access to alternative sources of finance is

20 These results are available in the Internet Appendix.21 The case study of L.A. Gear by DeAngelo, DeAngelo, and Wruck (2002) provides additional

evidence of the mechanisms documented in our study. They document that violations led to re-ductions in credit for L.A. Gear after covenant violations, ultimately reducing availability from$360 million to $25 million.


either limited or relatively costly. Thus, covenant violations play a key role indetermining the flow of credit to firms because of their ability to allocate controlrights to creditors in a state-contingent manner.

While the focus of this study has been on identifying and quantifying the roleof incentive conflicts and control rights in shaping financial policy, we believethat the perspective taken here can potentially shed light on several unresolvedissues in capital structure. For example, recent research (e.g., Molina (2005),Almeida and Philippon (2006), and Korteweg (2006)) has focused on alterna-tive measures of bankruptcy costs to help explain debt conservatism (Graham(2000)). Similarly, numerous theoretical and empirical studies assume thatfirms’ aversion to high leverage is driven by expected bankruptcy costs (e.g.,Bradley, Jarrell, and Kim (1984), Fischer, Heinkel, and Zechner (1989), Leland(1994), Hovakimian (2006)). While a focus on improving the measurement ofbankruptcy costs may yield more realistic patterns for capital structure, CFOsrank bankruptcy cost considerations seventh in terms of their importance indebt financing decisions (Graham and Harvey (2001)).

Alternatively, CFOs rank maintenance of financial flexibility as the most im-portant reason for limiting debt financing. We believe that a consideration ofcontractual restrictions and creditor rights outside of bankruptcy may help ex-plain observed capital structures, and may provide an explanation that is morein line with survey evidence and recent theory on the importance of financialflexibility (DeAngelo and DeAngelo (2006)). In particular, our findings suggestthat firms may appear ex ante conservative given the expected consequencesassociated with potential covenant violations. In other words, knowing ex antethat debt contracts impose significant restrictions on corporate behavior andviolation of those restrictions impose significant consequences, managers maydecide to rely less than they otherwise would on debt financing. We look forwardto future research that pursues these possibilities.

Appendix: Variable Definitions and CovenantViolation Search Terms

This appendix details the variable construction for analysis of the Compustatsample. All cash flow statement variables are first disaggregated into quarterlyflows.

Total Sales = item 2Total Assets = item 44Book Debt = item 51 + item 45Net Equity Issuance = (item 84 – item 93)/lagged item 44Net Equity Is-

suance = (shrout(t) ∗ cfacshr(t) – shrout(t–1) ∗ cfacshr(t–1)) ∗ (prc(t)/cfacpr(t) +prc(t–1)/cfacpr(t–1)) [CRSP definition]

Net Debt Issuance = (book debt – lagged book debt)/lagged item 44Net Debt Issuance = (data86 – data92)/lagged item 44 [Statement of cash

flows definition]Market Value of Equity = item 14 ∗ item 61Book Value of Equity = item 44 – (item 54 + annual item 10) + item52


Tangible Assets = item 42Net Worth = item 44 – item 54Cash = item 36Net Working Capital = item 40 – item 49EBITDA = item 21Cash Flow = item 8 + item 5Net Income = item 69Interest Expense = item 22Abnormal Total Accruals = based on the study by DeFond and Jiambalvo

(1994). Total accruals are first constructed from the statement of cash flows asthe difference between cash flow (item 76, adjusted for aggregation) and netcash flow from operating activities (item 108, adjusted for aggregation), normal-ized by the start-of-period assets. For each firm, this measure is then regressedagainst 1/assets (item 44), the change in operating income (item 21) normalizedby start-of-period assets (item 44), and tangible assets (item 42) normalized bystart-of-period assets (item 44). The residuals from these regressions form theabnormal total accruals.

Abnormal Working Capital Accruals = identical to Abnormal Total Accrualsbut for the use of working capital accruals, defined as the change in inventory(item 38), plus the change in accounts receivable (item 37), plus the change inother current assets (item 39), less the change in accounts payable (item 46),less the change in income taxes payable (item 47), less the change in othercurrent liabilities (item 48).

Abnormal Current Accruals = is an annual measure using annual Compus-tat data and is based on the study by Teoh, Welch, and Wong (1998), and whosederivation follows closely that found in Bharath, Sunder, and Sunder (2008).Total current accruals are first constructed from the statement of cash flows(Hribar and Collins (2002)) as the sum of minus the change in accounts receiv-ables, the change in inventory, the change in accounts payables, the change intaxes payable, and the change in other current assets. Total current accrualsare then normalized by last period’s total assets and regressed on two variables:(1) the inverse of last period’s total assets and (2) the change in sales normal-ized by last period’s total assets. The regression is run separately for each yearand each of the Fama and French 38 industry groups. The parameter estimatesfrom these regressions are then used to compute the normal current accrualsfor each firm in a particular industry-year as the predicted values from theregression. One modification, however, is that the second regressor fromthe regression is replaced by the difference between the change in sales andthe change in accounts receivables normalized by the start-of-period total as-sets for the computation of normal current accruals. The difference between theactual current accruals and the normal current accruals are abnormal currentaccruals.

The covenant violation search terms are as follows:

“in violation of covenant,” “in violation of a covenant,” “in default of covenant,”“in default of a covenant,” “in technical violation of covenant,” “in technicalviolation of a covenant,” “in violation of financial covenant,” “in violation of a


financial covenant,” “in default of financial covenant,” “in default of a financialcovenant,” “in technical violation of financial covenant,” “in technical violationof a financial covenant,” “in technical default of financial covenant,” “in tech-nical default of a financial covenant,” “not in compliance,” “out of compliance,”“received waiver,” “received a waiver,” “obtained waiver,” “obtained a waiver.”

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